Geodesic
A zero-inflated geometric INAR(1) process with random coefficient
Applications of Mathematics, Tome 63 (2018) no. 1, pp. 79-105.

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Many real-life count data are frequently characterized by overdispersion, excess zeros and autocorrelation. Zero-inflated count time series models can provide a powerful procedure to model this type of data. In this paper, we introduce a new stationary first-order integer-valued autoregressive process with random coefficient and zero-inflated geometric marginal distribution, named ZIGINAR$_{\rm RC}(1)$ process, which contains some sub-models as special cases. Several properties of the process are established. Estimators of the model parameters are obtained and their performance is checked by a small Monte Carlo simulation. Also, the behavior of the inflation parameter of the model is justified. We investigate an application of the process using a real count climate data set with excessive zeros for the number of tornados deaths and illustrate the best performance of the proposed process as compared with a set of competitive INAR(1) models via some goodness-of-fit statistics. Consequently, forecasting for the data is discussed with estimation of the transition probability and expected run length at state zero. Moreover, for the considered data, a test of the random coefficient for the proposed process is investigated.
DOI : 10.21136/AM.2018.0082-17
Classification : 62M10
Keywords: randomized binomial thinning; geometric minima; estimation; likelihood ratio test; mixture distribution; realization with random size 
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     title = {A zero-inflated geometric {INAR(1)} process with random coefficient},
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Bakouch, Hassan S.; Mohammadpour, Mehrnaz; Shirozhan, Masumeh. A zero-inflated geometric INAR(1) process with random coefficient. Applications of Mathematics, Tome 63 (2018) no. 1, pp. 79-105. doi : 10.21136/AM.2018.0082-17. http://geodesic.mathdoc.fr/articles/10.21136/AM.2018.0082-17/

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